User customizable data processing plan in a dispersed storage network

ABSTRACT

A method includes obtaining, by a user computing device of a dispersed storage network (DSN), a DSN access request and determining a custom data processing plan for the DSN access request. The method further includes identifying two or more dispersed storage (DS) processing units of the DSN as candidates to process the DSN access request. The method further includes determining DS processing capabilities of each of the two or more DS processing units and selecting a DS processing unit to process the DSN access request based on a favorable comparison of the DS processing capabilities and the custom data processing plan. The method further includes selecting DS processing options of the DS processing unit to process the DSN access request according to the custom data processing plan, and sending the DSN access request and selection of the one or more DS processing options to the DS processing unit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority pursuant to 35 U.S.C. § 120 as acontinuation-in-part of U.S. patent application Ser. No. 14/325,433entitled “SLICE MIGRATION IN A DISPERSED STORAGE NETWORK,” filed Jul. 8,2014, which claims priority claims priority pursuant to 35 U.S.C. § 120as a continuation-in-part of U.S. patent application Ser. No. 12/903,209entitled, “REVISION SYNCHRONIZATION OF A DISPERSED STORAGE NETWORK,”filed Oct. 13, 2010, issued as U.S. Pat. No. 9,152,489 on Oct. 6, 2015,which claims priority pursuant to 35 USC § 119 to U.S. ProvisionalApplication No. 61/290,775 entitled “DISTRIBUTED STORAGE DATASYNCHRONIZATION,” filed Dec. 29, 2009, all of which are herebyincorporated herein by reference in their entirety and made part of thepresent U.S. Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not Applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of the dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 10 is a flowchart illustrating an example of selecting a dispersedstorage (DS) processing unit in accordance with the invention;

FIG. 11 is a schematic block diagram of another embodiment of thedispersed or distributed storage network (DSN) in accordance with thepresent invention; and

FIG. 12 is a flowchart illustrating an example of selecting a dispersedstorage (DS) processing unit according to a custom data processing planin accordance with the invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage (DS) processing agent (i.e., a DSprocessing unit) for computing device 14. In this role, computing device16 dispersed storage error encodes and decodes data on behalf ofcomputing device 14. With the use of dispersed storage error encodingand decoding, the DSN 10 is tolerant of a significant number of storageunit failures (the number of failures is based on parameters of thedispersed storage error encoding function) without loss of data andwithout the need for a redundant or backup copies of the data. Further,the DSN 10 stores data for an indefinite period of time without dataloss and in a secure manner (e.g., the system is very resistant tounauthorized attempts at accessing the data).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 (i.e., user computing devices 12-14)individually or as part of a group of user devices. As a specificexample, the managing unit 18 coordinates creation of a vault (e.g., avirtual memory block associated with a portion of an overall namespaceof the DSN) within the DSN memory 22 for a user device, a group ofdevices, or for public access and establishes per vault dispersedstorage (DS) error encoding parameters for a vault. The managing unit 18facilitates storage of DS error encoding parameters for each vault byupdating registry information of the DSN 10, where the registryinformation may be stored in the DSN memory 22, a computing device12-16, the managing unit 18, and/or the integrity processing unit 20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of an embodiment of the dispersed ordistributed storage network (DSN). As illustrated, the DSN includes aplurality of user devices 1-u (e.g., user computing devices 12-14 ofFIG. 1), a plurality of DS processing units 1-p (e.g., computing device16 of FIG. 1), and a DSN memory 22. In an example of operation, the userdevice 1 may determine a DS processing unit 3 to utilize based onmatching DS processing unit attributes to DS processing unitrequirements. In another example, user device 2 determines to utilize DSprocessing unit 3 when DS processing unit 3 has the most favorableavailability history of the plurality of DS processing units 1-p and DSprocessing unit 3 is expected to continue to be available at a levelthat compares favorably with the user device 2 DS processing unitrequirements.

In another example of operation, the user device 6 may determine a DSprocessing unit 5 to utilize based on a predetermination and/orinitially on a predetermination followed by a potential subsequentmodification based in part on actual performance. In another example,user device 3 determines to initially utilize DS processing unit 1 whenDS processing unit 1 is listed in a predetermined table. Next, userdevice 3 determines to subsequently utilize DS processing unit 2 when DSprocessing unit 1 does not perform to a required level and DS processingunit 2 is the second choice.

In an example of operation, user device 7 provides DSN memory accessauthorization credentials when accessing the DSN memory 22 via DSprocessing unit 10. Next, the DS processing unit 10 verifies theauthorization credentials. The DS processing unit 10 forwards a DSNmemory access request to the DSN memory 22 when the authorizationcredential verification is favorable (e.g., on a list of authorizedusers for the particular item in the DSN memory 22). The DS processingunit 10 does not forward a DSN memory access request to the DSN memory22 when the authorization credential verification is not favorable(e.g., not on a list of authorized users for the particular item in theDSN memory 22). The method of operation of the user device 1-u todetermine the DS processing unit 1-p is discussed in greater detail withreference to FIG. 10.

In another example, DS processing unit 3 forwards the authorizationcredentials to the DSN memory 22 with the DSN memory access request(e.g., without verification by the DS processing unit 3). The DSN memory22 verifies the authorization credentials. The DSN memory 22 processesthe memory access request when the authorization credential verificationis favorable. The DSN memory 22 does not process the memory accessrequest when the authorization credential verification is not favorable.

FIG. 10 is a flowchart illustrating an example of selecting a dispersedstorage (DS) processing unit. The method begins at step 82 where aprocessing module (e.g., of a user device) determines dispersed storagenetwork (DSN) memory access requirements. The requirements may includeone or more of security requirements, performance requirements, andpriority requirements. Such a determination may be based on one or moreof a query, a data type, a data size, a security indicator, aperformance indicator, a command, a predetermination, and a lookup.

The method continues at step 84 where the processing module determinescandidate DS processing units based on one or more of a virtual DSNaddress to physical location table, a query, a message from one or moreDS processing units, a data type, a data size, a security indicator, aperformance indicator, a status indicator, a command, apredetermination, and a lookup. The method continues at step 86 wherethe processing module determines candidate DS processing unitsattributes where the attributes may include one or more of currentcapacity, current loading, uptime history, performance history, datatypes supported, data types not supported, security restrictions, andencryption algorithms supported. Such a determination may be based onone or more of a virtual DSN address to physical location table, aquery, a message from one or more DS processing units, a data type, adata size, a security indicator, a performance indicator, a command, apredetermination, and a lookup. In an example, the processing moduledetermines that DS processing unit 1 has an attribute of capacity abovea threshold based on the performance indicator. In another example, theprocessing module determines that DS processing unit 4 has an attributeof a particular encryption algorithm based on the security indicatorfrom a query.

The method continues at step 88 where the processing module determines aDS processing unit to utilize based on one or more of the DSN accessrequirements, the candidate DS processing units, the candidateprocessing units attributes, a comparison of the candidate processingunits attributes to the DSN access requirements, a virtual DSN addressto physical location table, a query, a message from one or more DSprocessing units, a data type, a data size, a security indicator, aperformance indicator, a command, a predetermination, and a lookup. Inan example, the processing module determines the DS processing unit suchthat substantially all of the requirements are met or exceeded. Forinstance, the processing module determines the DS processing unit thatmeets or exceeds the most requirements. The method continues at step 90where the processing module utilizes the determined DS processing unitfor the DSN access (e.g., store, retrieve, delete, check status).

FIG. 11 is a schematic block diagram of another embodiment of thedispersed or distributed storage network (DSN) that includes a pluralityof user computing devices 1-n (e.g., user computing devices 12-14 ofFIG. 1), a plurality of DS processing units 1-x (e.g., computing device16 of FIG. 1), and a DSN memory 22.

In an example of operation, a user computing device of the usercomputing devices 1-n obtains a DSN access request (e.g., a request tostore, retrieve, delete, check status, integrity check, etc., datato/from the DSN memory 22). For example user computing device 1 obtainsDSN access request 1, user computing device 2 obtains DSN access request2, user computing device 3 obtains DSN access request 3, and usercomputing device n obtains DSN access request n. The user computingdevice determines a custom data processing plan to process the DSNaccess request. The user computing device determines the custom dataprocessing plan by selecting dispersed storage error encoding parametersfor storing data of the DSN access request in accordance with a desiredlevel of security requirement (e.g., for a higher desired level ofsecurity, per encoded data slice encryption, compression, and/orintegrity checksums are selected), selecting dispersed storage errorencoding parameters for storing data of the DSN access request inaccordance with a desired level of performance requirement (e.g., for afast read/write DSN access request, a small decode threshold is selectedand for a long term store with slow write/read (i.e., archive) a largerdecode threshold is selected), and/or selecting dispersed storage errorencoding parameters for storing data of the DSN access request inaccordance with a desired level of access requirement (e.g., if highfrequency reading/writing of data of the DSN access request is expected,a smaller decode threshold number is selected).

For example, DSN access request 1 may be a request to write highdefinition (HD) video with moderate security, high performance, and highfrequency of access. User computing device 1 determines a custom dataprocessing plan that includes error encoding parameters for storing dataof DSN access request 1 in accordance with a moderate level of security(e.g., per encoded data slice encryption and compression are selected),error encoding parameters for storing data of DSN access request 1 inaccordance with high performance (e.g., a small decode threshold isselected), and error encoding parameters for storing data of DSN accessrequest 1 in accordance with high frequency of access (e.g., a smalldecode threshold number is selected).

The custom data processing plan may also include user selected customrebuild options. For example, the custom data processing plan mayindicate that after x number of years, rebuilding of data is no longerrequired. As another example, the custom data processing plan mayindicate that rebuilding should occur only if two or more encoded dataslices require rebuilding.

The user computing device identifies two or more (DS) processing unitsof DS processing units 1-x of the DSN as possible candidates to processthe DSN access request. The user computing device may identify the twoor more DS processing units by accessing a virtual DSN table thatincludes, for the DS processing units 1-x, information regardingavailability and one or more of: a data type, a data size, a securityindicator, a performance indicator, and a status indicator.Alternatively, or additionally, the user computing device may identifythe two or more DS processing units by receiving a command (e.g., a userinput) identifying the two or more DS processing units, performing alookup of available DS processing units of the plurality of DSprocessing units, identifying a predetermined two or more DS processingunits as the two or more DS processing units, and/or receiving messagesfrom DS processing units of the plurality of DS processing units 1-x inresponse to a query-response protocol to obtain the information.

When the information is obtained, (e.g., via accessing a DSN tableand/or querying the DS processing units 1-x) the user computing devicecorrelates the information regarding the one or more of: the data type,the data size, the security indicator, the performance indicator, andthe status indicator with the custom data processing plan. The usercomputing device can then identify the two or more DS processing unitsthat are not only available to process the DSN access request, but haveinformation that correlates with the custom data processing plan.

For example, user computing device 1 accesses a virtual DSN table andidentifies DS processing unit 1, DS processing unit 2, and DS processingunit x as available to process DSN access request 1. The virtual DSNtable also indicates information that DS processing unit x processes lowresolution (res) video and photos. Because this information does notcorrelate with user computing device 1's custom data processing plan(i.e., to process HD video), DS processing unit x is eliminated as acandidate and DS processing unit 1 and DS processing unit 2 are left asoptions.

The user computing device determines DS processing capabilities of eachof the two or more DS processing units identified as candidates. The DSprocessing capabilities include one or more of current capacity, currentloading, uptime history, performance history, data types supported(e.g., high definition (HD) and above video, low resolution video,etc.), data types not supported, DSN access requests supported (e.g.,the DS processing unit is a dedicated write or read unit), securityrestrictions, and encryption algorithms supported.

For example, user computing device 1 determines that DS processing unit1 has DS processing capabilities of processing HD and above video and DSprocessing unit 2 has DS processing capabilities to perform short termfast stores and reads. However, DS processing unit 2 has a history oflow performance and does not support processing of HD video.

The user computing device selects a DS processing unit of the two ormore DS processing units to process the DSN access request based on afavorable comparison of the DS processing capabilities of the DSprocessing unit and the custom data processing plan. For example, whileDS processing unit 2 has DS processing capabilities to process shortterm fast stores and reads which compares favorably to computing device1's custom data processing plan, DS processing unit 2's DS processingcapabilities also indicate that it does not process HD video and that ithas a history of poor performance. Therefore, user computing device 1selects DS processing unit 1 to process DSN access request 1 because DSprocessing unit 1's DS processing capabilities compare most favorably touser computing device 1's custom data processing plan.

The user computing device selects one or more DS processing options ofthe DS processing unit to process the DSN access request according tothe custom data processing plan. The one or more one or more DSprocessing options include one or more of a compression type, encryptiontype, encoding function type (e.g., IDA type), and rebuilding options.For example, user computing device 1's custom data processing planindicates a high security level, high performance, and high frequency ofaccess. Therefore, user computing device 1 selects DS processing optionsof DS processing unit 1 according to those requirements.

The user computing device sends the DSN access request and selection ofthe one or more DS processing options to the DS processing unit, wherethe DS processing unit processes the DSN access request in accordancewith the one or more DS processing options.

FIG. 12 is a flowchart illustrating an example of selecting a dispersedstorage (DS) processing unit according to a custom data processing plan.The method begins with step 92 where the a user computing device of aplurality of user computing devices of the DSN obtains a DSN accessrequest (e.g., a request to store, retrieve, delete, check status,integrity check, etc., data to/from the DSN memory 22). The methodcontinues with step 94 where the user computing device determines acustom data processing plan to process the DSN access request. The usercomputing device determines the custom data processing plan by selectingdispersed storage error encoding parameters for storing data of the DSNaccess request in accordance with a desired level of securityrequirement (e.g., for a higher desired level of security, per encodeddata slice encryption, compression, and/or integrity checksums areselected), selecting dispersed storage error encoding parameters forstoring data of the DSN access request in accordance with a desiredlevel of performance requirement (e.g., for a fast read/write DSN accessrequest, a small decode threshold is selected and for a long term storewith slow write/read (i.e., archive) a larger decode threshold isselected), and/or selecting dispersed storage error encoding parametersfor storing data of the DSN access request in accordance with a desiredlevel of access requirement (e.g., if high frequency reading/writing ofdata of the DSN access request is expected, a smaller decode thresholdnumber is selected).

The custom data processing plan may also include user selected customrebuild options. For example, the custom data processing plan mayindicate that after x number of years, rebuilding of data is no longerrequired. As another example, the custom data processing plan mayindicate that rebuilding should occur only if two or more encoded dataslices require rebuilding.

The method continues with step 96 where the user computing deviceidentifies two or more (DS) processing units of a plurality of DSprocessing units of the DSN as possible candidates to process the DSNaccess request. The user computing device may identify the two or moreDS processing units by accessing a virtual DSN table that includes, forthe plurality of DS processing units, information regarding availabilityand one or more of: a data type, a data size, a security indicator, aperformance indicator, and a status indicator. Alternatively, oradditionally, the user computing device may identify the two or more DSprocessing units by receiving a command (e.g., a user input) identifyingthe two or more DS processing units, performing a lookup of available DSprocessing units of the plurality of DS processing units, identifying apredetermined two or more DS processing units as the two or more DSprocessing units, and/or receiving messages from DS processing units ofthe plurality of DS processing units in response to a query-responseprotocol to obtain the information.

When the information is obtained, (e.g., via accessing a DSN tableand/or querying the plurality of DS processing units) the user computingdevice correlates the information regarding the one or more of: the datatype, the data size, the security indicator, the performance indicator,and the status indicator with the custom data processing plan. The usercomputing device can then identify the two or more DS processing unitsthat are not only available to process the DSN access request, but haveinformation that correlates with the custom data processing plan.

The method continues with step 98 where the user computing devicedetermines DS processing capabilities of each of the two or more DSprocessing units identified as candidates. The DS processingcapabilities include one or more of current capacity, current loading,uptime history, performance history, data types supported (e.g., highdefinition (HD) and above video, low resolution video, etc.), data typesnot supported, DSN access requests supported (e.g., the DS processingunit is a dedicated write or read unit), security restrictions, andencryption algorithms supported.

The method continues with step 100 where the user computing deviceselects a DS processing unit of the two or more DS processing units toprocess the DSN access request based on a favorable comparison of the DSprocessing capabilities of the DS processing unit and the custom dataprocessing plan. The method continues with step 102 where the usercomputing device selects one or more DS processing options of the DSprocessing unit to process the DSN access request according to thecustom data processing plan. The one or more one or more DS processingoptions include one or more of a compression type, encryption type,encoding function type (e.g., IDA type), and rebuilding options.

The method continues with step 102 where the user computing device sendsthe DSN access request and selection of the one or more DS processingoptions to the DS processing unit, where the DS processing unitprocesses the DSN access request in accordance with the one or more DSprocessing options.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/−1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, micro-controller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

While the transistors in the above described figure(s) is/are shown asfield effect transistors (FETs), as one of ordinary skill in the artwill appreciate, the transistors may be implemented using any type oftransistor structure including, but not limited to, bipolar, metal oxidesemiconductor field effect transistors (MOSFET), N-well transistors,P-well transistors, enhancement mode, depletion mode, and zero voltagethreshold (VT) transistors.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form asolid-state memory, a hard drive memory, cloud memory, thumb drive,server memory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method comprises: obtaining, by a usercomputing device of a plurality of user computing devices of a dispersedstorage network (DSN), a DSN access request; determining, by the usercomputing device, a custom data processing plan to process the DSNaccess request; identifying, by the user computing device, two or moredispersed storage (DS) processing units of a plurality of DS processingunits of the DSN as possible candidates to process the DSN accessrequest; determining, by the user computing device, DS processingcapabilities of each of the two or more DS processing units; selecting,by the user computing device, a DS processing unit of the two or more DSprocessing units to process the DSN access request based on a favorablecomparison of the DS processing capabilities of the DS processing unitand the custom data processing plan; selecting, by the user computingdevice, one or more DS processing options of the DS processing unit toprocess the DSN access request according to the custom data processingplan; and sending, by the user computing device, the DSN access requestand selection of the one or more DS processing options to the DSprocessing unit, wherein the DS processing unit processes the DSN accessrequest in accordance with the one or more DS processing options.
 2. Themethod of claim 1, wherein the determining the custom data processingplan includes one or more of: selecting, by the user computing device,dispersed storage error encoding parameters for storing data of the DSNaccess request in accordance with a desired level of securityrequirement; selecting, by the user computing device, the dispersedstorage error encoding parameters for storing the data of the DSN accessrequest in accordance with a desired level of performance requirement;and selecting, by the user computing device, the dispersed storage errorencoding parameters for storing the data of the DSN access request inaccordance with a desired level of access requirement.
 3. The method ofclaim 1, wherein the identifying the two or more DS processing unitscomprises one or more of: accessing, by the user computing device, avirtual DSN table that includes, for the plurality of DS processingunits, information regarding availability and one or more of: a datatype, a data size, a DSN access request processing type, a securityindicator, a performance indicator, and a status indicator; receiving,by the user computing device, a command identifying the two or more DSprocessing units; performing, by the user computing device, a lookup ofavailable DS processing units of the plurality of DS processing units;identifying, by user computing device, a predetermined two or more DSprocessing units as the two or more DS processing units; and receiving,by the user computing device, messages from DS processing units of theplurality of DS processing units in response to a query-responseprotocol to obtain the information.
 4. The method of claim 3, whereinthe identifying the two or more DS processing units further comprises:when the information is obtained, correlating, by the user computingdevice, the information regarding the one or more of: the data type, thedata size, the security indicator, the performance indicator, and thestatus indicator with the custom data processing plan; and identifying,by the user computing device, DS processing units having informationthat correlates with the custom data processing plan as the two or moreDS processing units.
 5. The method of claim 1, wherein the DS processingcapabilities include one or more of: current capacity; current loading;uptime history; performance history; data types supported; data typesnot supported; DSN access requests supported; security restrictions; andencryption algorithms supported.
 6. The method of claim 1, wherein theDS processing options include one or more of: compression type;encryption type; encoding function type; and rebuilding options.
 7. Auser computing device of a plurality of user computing devices of adispersed storage network (DSN), the user computing device comprises: aninterface; memory; and a processing module operably coupled to thememory and the interface, wherein the processing module is operable to:obtain a DSN access request; determine a custom data processing plan toprocess the DSN access request; identify two or more dispersed storage(DS) processing units of a plurality of DS processing units of the DSNas possible candidates to process the DSN access request; determining,by the user computing device, DS processing capabilities of each of thetwo or more DS processing units; select a DS processing unit of the twoor more DS processing units to process the DSN access request based on afavorable comparison of the DS processing capabilities of the DSprocessing unit and the custom data processing plan; select one or moreDS processing options of the DS processing unit to process the DSNaccess request according to the custom data processing plan; and sendthe DSN access request and selection of the one or more DS processingoptions to the DS processing unit, wherein the DS processing unitprocesses the DSN access request in accordance with the one or more DSprocessing options.
 8. The user computing device of claim 7, wherein theprocessing module is operable to determine the custom data processingplan by one or more of: selecting dispersed storage error encodingparameters for storing data of the DSN access request in accordance witha desired level of security requirement; selecting the dispersed storageerror encoding parameters for storing the data of the DSN access requestin accordance with a desired level of performance requirement; andselecting the dispersed storage error encoding parameters for storingthe data of the DSN access request in accordance with a desired level ofaccess requirement.
 9. The user computing device claim 7, wherein theprocessing module is operable to identify the two or more DS processingunits by one or more of: accessing a virtual DSN table that includes,for the plurality of DS processing units, information regardingavailability and one or more of: a data type, a data size, a DSN accessrequest processing type, a security indicator, a performance indicator,and a status indicator; receiving a command identifying the two or moreDS processing units; performing a lookup of available DS processingunits of the plurality of DS processing units; identifying apredetermined two or more DS processing units as the two or more DSprocessing units; and receiving messages from DS processing units of theplurality of DS processing units in response to a query-responseprotocol to obtain the information.
 10. The user computing device ofclaim 9, wherein the processing module is operable to further identifythe two or more DS processing units by: when the information isobtained, correlating the information regarding the one or more of: thedata type, the data size, the security indicator, the performanceindicator, and the status indicator with the custom data processingplan; and identifying DS processing units having information thatcorrelates with the custom data processing plan as the two or more DSprocessing units.
 11. The user computing device of claim 7, wherein theDS processing capabilities include one or more of: current capacity;current loading; uptime history; performance history; data typessupported; data types not supported; DSN access requests supported;security restrictions; and encryption algorithms supported.
 12. The usercomputing device of claim 7, wherein the DS processing options includeone or more of: compression type; encryption type; encoding functiontype; and rebuilding options.
 13. A computer readable memory comprises:a first memory element that stores operational instructions that, whenexecuted by a user computing device of a plurality of user computingdevices of a dispersed storage network (DSN), causes the user computingdevice to: obtain a DSN access request; determine a custom dataprocessing plan to process the DSN access request; identify two or moredispersed storage (DS) processing units of a plurality of DS processingunits of the DSN as possible candidates to process the DSN accessrequest; determining, by the user computing device, DS processingcapabilities of each of the two or more DS processing units; select a DSprocessing unit of the two or more DS processing units to process theDSN access request based on a favorable comparison of the DS processingcapabilities of the DS processing unit and the custom data processingplan; select one or more DS processing options of the DS processing unitto process the DSN access request according to the custom dataprocessing plan; and send the DSN access request and selection of theone or more DS processing options to the DS processing unit, wherein theDS processing unit processes the DSN access request in accordance withthe one or more DS processing options.
 14. The computer readable memoryof claim 13, wherein the first memory element further stores operationalinstructions that, when executed by the user computing device, causesthe user computing device to determine the custom data processing planby one or more of: selecting dispersed storage error encoding parametersfor storing data of the DSN access request in accordance with a desiredlevel of security requirement; selecting the dispersed storage errorencoding parameters for storing the data of the DSN access request inaccordance with a desired level of performance requirement; andselecting the dispersed storage error encoding parameters for storingthe data of the DSN access request in accordance with a desired level ofaccess requirement.
 15. The computer readable memory of claim 13,wherein the first memory element further stores operational instructionsthat, when executed by the user computing device, causes the usercomputing device to identify the two or more DS processing units by oneor more of: accessing a virtual DSN table that includes, for theplurality of DS processing units, information regarding availability andone or more of: a data type, a data size, a DSN access requestprocessing type, a security indicator, a performance indicator, and astatus indicator; receiving a command identifying the two or more DSprocessing units; performing a lookup of available DS processing unitsof the plurality of DS processing units; identifying a predetermined twoor more DS processing units as the two or more DS processing units; andreceiving messages from DS processing units of the plurality of DSprocessing units in response to a query-response protocol to obtain theinformation.
 16. The computer readable memory of claim 15, wherein thefirst memory element further stores operational instructions that, whenexecuted by the user computing device, causes the user computing deviceto further identify the two or more DS processing units by one or moreof: when the information is obtained, correlating the informationregarding the one or more of: the data type, the data size, the securityindicator, the performance indicator, and the status indicator with thecustom data processing plan; and identifying DS processing units havinginformation that correlates with the custom data processing plan as thetwo or more DS processing units.
 17. The computer readable memory ofclaim 13, wherein the DS processing capabilities include one or more of:current capacity; current loading; uptime history; performance history;data types supported; data types not supported; DSN access requestssupported; security restrictions; and encryption algorithms supported.18. The computer readable memory of claim 13, wherein the DS processingoptions include one or more of: compression type; encryption type;encoding function type; and rebuilding options.